ALBERT

Description: A simple method for the controllable synthesis of Au nanocrystals–metal selenide hybrid nanostructures via amino acid guiding strategy is proposed. The results show that the symmetric overgrowth mode of PbSe shells on Au nanorods can be precisely manipulated by only adjusting the initial concentration of Pb2+. The shape of Au–PbSe hybrids can evolve from dumbbell-like to yolk-shell. Interestingly, the plasmonic absorption enhancement could be tuned by the symmetry of these hybrid nanostructures. This provides an effective pathway for maneuvering plasmon-induced energy transfer in metal–semiconductor hybrids. In addition, the photoactivities of Au–PbSe nanorods sensitized TiO2 electrodes have been further evaluated. Owing to the synergism between effective plasmonic enhancement effect and efficient interfacial charge transfer in these hybrid nanostructures, the Au–PbSe yolk-shell nanorods exhibit an outstanding photocurrent activity. Their photocurrent density is 4.38 times larger than that of Au–PbSe dumbbell-like nanorods under light irradiation at λ 〉 600 nm. As a versatile method, the proposed strategy can also be employed to synthesize other metal–selenide hybrid nanostructures (such as Au–CdSe, Au–Bi2Se3 and Au–CuSe).

Description: The present paper reported a novel approach for the fabrication of a high-aspect ratio (K, Na)NbO3 (KNN) piezoelectric micropillar array via epoxy gelcasting, which involves the in situ consolidation of aqueous KNN suspensions with added hydantoin epoxy resin on a polydimethylsiloxane (PDMS) soft micromold. KNN suspensions with solid loadings of up to 45.0 vol.% have rheological behavior, which was suitable for the gelcasting process. The uniform green KNN bodies derived from the optimized suspension of 42.0 vol.% solid loading and 15.0 wt.% resin had exceptionally high mechanical strength (9.14 MPa), which was responsible for the integrity of the piezoceramic micropattern structure. The square-shaped KNN piezoelectric pillar array with lateral dimensions of up to 5 μm and an aspect ratio of up to five was successfully fabricated.

Description: Piezoelectric fiber composites (PFC) have shown excellent performance in the areas of vibration control and deformation control. The viscosity and rigidity of the epoxy resin before and after curing, respectively, were very important factors that affected the performance of PFC. In this paper, Aradite 2020, DP 460, and DP 490 epoxy resins, with the viscosities of 0.15, 25.0, and 250.0 Pa·s, respectively, were employed to encapsulate the piezoelectric fiber composite. The PFC that was packaged with Araldite 2020 had the best free strain of 1420 ppm and tip displacement of 17.8 mm. DP 490 caused the lowest performance of PFC, due to the highest viscosity. When the environmental temperature increased from −40 to +80 °C, the free strain of PFC with Aradite 2020 increased at first and then decreased, reaching a maximum value of 1440 ppm at 30 °C, which was mainly related to the mismatch of the resin/ceramic thermal expansion coefficient.

Description: Operationalization of sustainability assessments is necessary to promote the sustainable development of agroecosystems. However, primarily, focus has been on the development of sustainability assessment tools with less attention on whether these are suitable for adoption and implementation in specific areas. This drawback could lead to inappropriate management guidance for agricultural practices. Hence, three extensively used models, i.e., the Driver–Pressure–State–Impact–Response (DPSIR) framework, ecological footprint (EF), and emergy analysis (EMA), were applied to quantify the sustainability performance of the agroecosystems in 27 cities in the Yangtze River Delta Urban Agglomeration (YRDUA), China, in 2016. The models were compared using the Pearson correlation analysis and natural break method, to determine a more adaptive method for agricultural sustainability assessments. The level of agricultural sustainable development of each city varied according to the methodology considered for its calculation. Compared with the EMA model, the DPSIR and EF models showed a better relationship (Pearson correlation coefficient of 0.71). The DPSIR model more accurately represented regional rankings of the agricultural sustainability at the municipality level due to its comprehensive consideration of multiple dimension factors and significance for policy making. However, each methodology has its own contribution depending on the study objectives. Hence, different models should be used for adequate determination of agricultural sustainable development at different regional scales; this would also enable better implementation of agricultural practices as well as policies in any given agricultural area for promoting the sustainable development of agroecosystems.

Description: Global greening over the past 30 years since 1980s has been confirmed by numerous studies. However, a single-dimensional indicator and non-spatial modelling approaches might exacerbate uncertainties in our understanding of global change. Thus, comprehensive monitoring for vegetation’s various properties and spatially explicit models are required. In this study, we used the newest enhanced vegetation index (EVI) products of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 to detect the inconsistency trend of annual peak and average global vegetation growth using the Mann–Kendall test method. We explored the climatic factors that affect vegetation growth change from 2001 to 2018 using the spatial lag model (SLM), spatial error model (SEM) and geographically weighted regression model (GWR). The results showed that EVImax and EVImean in global vegetated areas consistently showed linear increasing trends during 2001–2018, with the global averaged trend of 0.0022 yr−1 (p 〈 0.05) and 0.0030 yr−1 (p 〈 0.05). Greening mainly occurred in the croplands and forests of China, India, North America and Europe, while browning was almost in the grasslands of Brazil and Africa (18.16% vs. 3.08% and 40.73% vs. 2.45%). In addition, 32.47% of the global vegetated area experienced inconsistent trends in EVImax and EVImean. Overall, precipitation and mean temperature had positive impacts on vegetation variation, while potential evapotranspiration and vapour pressure had negative impacts. The GWR revealed that the responses of EVI to climate change were inconsistent in an arid or humid area, in cropland or grassland. Climate change could affect vegetation characteristics by changing plant phenology, consequently rendering the inconsistency between peak and mean greening. In addition, anthropogenic activities, including land cover change and land use management, also could lead to the differences between annual peak and mean vegetation variations.